### Table 5 Longitudinal model fitting results by Cholesky decomposition

"... In PAGE 6: ... The discrepancy in variances is consistent with the presence of a con- trast effect. Table5 contains an overview of the results of the longitudinal analyses. Model 1 serves as the baseline model.... ..."

### Table 3 Longitudinal

"... In PAGE 4: ...2. Longitudinal genetic analyses of RTs from test occasion I and II Twin correlations for RTs on both occasions are given in Table3 . The fit of the ACE model (Table 3) was compared against the saturated model.... ..."

### Table 2 Longitudinal Asymmetry

"... In PAGE 8: ... The present paper is a step towards a systematic investigation of growth rate asymmetry in disaggregate price series, and towards ruling out the possibility that this is caused by asymmetry in money growth. Table2 reports triples statistics of growth rates for the 29 PPI components and the 36 CPI components at the monthly, quarterly and annual frequencies. Positive growth rate asymmetry prevails in the vast majority of these components, though significance levels tend to fall with higher levels of temporal aggregation.... ..."

### Table 3 Longitudinal Inference Subspaces

### Table 2: Autocorrelation times = 1 + 2 P1 k=1 (k), where (k) is the autocorrelation at lag k for the parameter of interest, for MCMC algorithms in the ddI/ddC data model 4.2 Longitudinal binary observations, single random e ect Our numerical illustration for this model considers a subset of data from the Six Cities study, a longitudinal study of the health e ects of air pollution (see e.g. Fitzmaurice and Laird (1993) for the data and a likelihood-based analysis). The data consist of repeated binary measurements yij of the wheezing status (1 = yes, 0 = no) of child i at time j, i = 1; : : :; I; : j = 1; : : :J, for each of I = 537 children living in Stuebenville, Ohio at J = 4 timepoints. We are given two predictor variables: aij, the age of child i in years at measurement point j (7, 8, 9, or 10 years), and si, the smoking status of child i apos;s mother (1 = yes, 0 = no). Following the Bayesian analysis of Chib and Greenberg (1998), we adopt the conditional response model

1999

"... In PAGE 14: ... While lag 1 autocorrelations are a good predictor of MCMC algorithm performance, they of course do not tell the whole story. To summarize the autocorrelations at all lags and their overall rate of decay, Table2 gives the autocorrelation time = 1 + 2 P1 k=1 (k) for each parameter in Table 1, where (k) is the autocorrelation at lag k for the parameter of interest. We estimated using the sample autocorrelations estimated from the MCMC chain, cutting o the summation when these dropped below 0.... In PAGE 14: ...ample sizes (Kass et al., 1998, p.99) as the MCMC sample size, G, divided by . Thus can be interpreted as the relative increase in run length necessitated by the Markov dependence. The autocorrelation times in Table2 reveal an essentially similar pattern to that for the lag 1 autocorrelations in Table 1. Algorithm 1 has substantial autocorrelation times for almost all parameters.... ..."

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### Table 4. Longitudinal eigenvalues, Stabilator trimmed case

"... In PAGE 34: ...1204 -1.2215 Table4 (continued). Longitudinal eigenvalues, Stabilator trimmed case... ..."

### Table 4. Longitudinal eigenvalues, Stabilator trimmed case

"... In PAGE 34: ...1204 -1.2215 Table4 (continued). Longitudinal eigenvalues, Stabilator trimmed case... ..."

### Table 3. Longitudinal traction test results.

2007

"... In PAGE 5: ...able 2. Surface specifications ................................................................................................................ 4 Table3 .... In PAGE 23: ... A typical set of longitudinal traction test results is included in Figure 5. The results of the longitudinal traction tests are summarized in Table3 for the two test stan- dards described earlier. 0 0.... ..."

### Table 2 Longitudinal Participation: Number of Twins

"... In PAGE 2: ... A total of 4591 pairs participated (793 incomplete and 3798 complete pairs). Table2 gives an overview of the longitudinal participation rate. Subjects could take part from one to five times.... ..."